d_model = 512  # Embedding Size  
d_ff = 2048  # FeedForward dimension  
d_k = d_v = 64  # dimension of K(=Q), V
n_layers = 6  # number of Encoder of Decoder Layer
n_heads = 8  # number of heads in Multi-Head Attention

class Transformer(nn.Module):
    def __init__(self):
        super(Transformer, self).__init__()
        self.encoder = Encoder()
        self.decoder = Decoder()
        # 输出层: 输入是d_model维的，输出是词表大小
        self.projection = nn.Linear(d_model, tgt_vocab_size, bias=False) 

    def forward(self, enc_inputs, dec_inputs):
        # enc_inputs : [batch_size, src_len], encoder的输入
		# dec_inputs : [batch_size, tgt_len], decoder的输入
        enc_outputs, enc_self_attns = self.encoder(enc_inputs)
        dec_outputs, dec_self_attns, dec_enc_attns = self.decoder(dec_inputs, enc_inputs, enc_outputs)
        dec_logits = self.projection(dec_outputs) # dec_logits : [batch_size x src_vocab_size x tgt_vocab_size]

        return dec_logits.view(-1, dec_logits.size(-1)), enc_self_attns, dec_self_attns, dec_enc_attns

class Encoder(nn.Module):
    def __init__(self):
        super(Encoder, self).__init__()
        # Embedding: 通过参数矩阵查表
        self.src_emb = nn.Embedding(src_vocab_size, d_model)  
        # 位置编码
        self.pos_emb = PositionalEncoding(d_model)
        # 或者直接使用已经准备好的embedding矩阵
        # self.pos_emb = nn.Embedding.from_pretrained(get_sinusoid_encoding_table(src_len+1, d_model),freeze=True)
        
        self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])

    def forward(self, enc_inputs): 
        # enc_inputs : [batch_size x source_len]
        # enc_outputs：[batch_size, src_len, d_model]
        enc_outputs = self.src_emb(enc_inputs) + self.pos_emb(torch.LongTensor([[1,2,3,4,0]]))
        enc_self_attn_mask = get_attn_pad_mask(enc_inputs, enc_inputs)
        enc_self_attns = []
        for layer in self.layers:
            enc_outputs, enc_self_attn = layer(enc_outputs, enc_self_attn_mask)
            enc_self_attns.append(enc_self_attn)
            
        return enc_outputs, enc_self_attns

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super(PositionalEncoding, self).__init__()

        # 直接对照着公式去敲代码，下面只是其中一种实现方式；
        # pos代表的是单词在句子中的索引，比如max_len是128个，那么索引就是从0，1，2，...,127
        # 假设我的dmodel是512，2i那个符号中i从0取到了255，那么2i对应取值就是0,2,4...510
        self.dropout = nn.Dropout(p=dropout)
        pe = torch.zeros(max_len, d_model)
        # unsqueeze(1): 在维度1上增加了一个新的维度，使得position的维度变为[max_len, 1]
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)   
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        # position * div_term：广播机制 [max_len, 1] * [d_model] = [max_len, d_model]
        # pe[:, 0::2]: 从0开始到最后面，步长为2，代表的就是偶数位置
        pe[:, 0::2] = torch.sin(position * div_term) 
        # pe[:, 1::2]: 从1开始到最后面，步长为2，代表的就是奇数位置
        pe[:, 1::2] = torch.cos(position * div_term) 
		# unsqueeze(0): [max_len, d_model] -> [1, max_len, d_model]
        # transpose(0,1): [1, max_len, d_model] -> [max_len, 1, d_model]
        pe = pe.unsqueeze(0).transpose(0, 1)

        self.register_buffer('pe', pe)  ## 定一个缓冲区，简单理解为这个参数不更新就可以
        
    def forward(self, x):
        """
        x: [seq_len, batch_size, d_model]
        """
        x = x + self.pe[:x.size(0), :]
        
        return self.dropout(x)

def get_attn_pad_mask(seq_q, seq_k):
    # seq_q 和 seq_k 是q/k的序列，不一定一致，例如在cross attention中，q来自解码端，k来自编码端
    batch_size, len_q = seq_q.size()
    batch_size, len_k = seq_k.size()
    # eq(zero) is PAD token，标记为1
    pad_attn_mask = seq_k.data.eq(0).unsqueeze(1)  # batch_size x 1 x len_k
    
    # expand: 将张量沿着某些维度进行扩展, expand方法并不会复制原始数据，而是使用广播的方式共享原始数据
    return pad_attn_mask.expand(batch_size, len_q, len_k)  # batch_size x len_q x len_k

class EncoderLayer(nn.Module):
    def __init__(self):
        super(EncoderLayer, self).__init__()
        self.enc_self_attn = MultiHeadAttention()
        self.pos_ffn = PoswiseFeedForwardNet()

    def forward(self, enc_inputs, enc_self_attn_mask):
        enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs, enc_self_attn_mask) # enc_inputs to same Q,K,V
        # enc_outputs: [batch_size x len_q x d_model]
        enc_outputs = self.pos_ffn(enc_outputs) 
        
        return enc_outputs, attn

class MultiHeadAttention(nn.Module):
    def __init__(self):
        super(MultiHeadAttention, self).__init__()
        # 参数矩阵, 用线性层的方式来表示
        self.W_Q = nn.Linear(d_model, d_k * n_heads)
        self.W_K = nn.Linear(d_model, d_k * n_heads)
        self.W_V = nn.Linear(d_model, d_v * n_heads)
        self.linear = nn.Linear(n_heads * d_v, d_model)
        self.layer_norm = nn.LayerNorm(d_model)

    def forward(self, Q, K, V, attn_mask):
        # Q: [batch_size, len_q, d_model], K: [batch_size, len_k, d_model], V: [batch_size, len_k, d_model]
        residual, batch_size = Q, Q.size(0)
        """
        投影->分头->交换张量维度： (B, S, D) -proj-> (B, S, D) -split-> (B, S, H, W) -trans-> (B, H, S, W)
        """
        # self.W_Q(Q): [batch_size, len_q, d_model] -> [batch_size, len_q, d_k * n_heads]
        # self.W_Q(Q).view(batch_size, -1, n_heads, d_k): [batch_size, len_q, d_k * n_heads]->[batch_size , len_q , n_heads, d_k]
        # transpose(1,2): 维度 1 和 2 交换了位置
                
        q_s = self.W_Q(Q).view(batch_size, -1, n_heads, d_k).transpose(1,2)  # q_s: [batch_size x n_heads x len_q x d_k]
        # 注意q和k分头之后维度是一致的，所以这里都是dk
        k_s = self.W_K(K).view(batch_size, -1, n_heads, d_k).transpose(1,2)  # k_s: [batch_size x n_heads x len_k x d_k]
        v_s = self.W_V(V).view(batch_size, -1, n_heads, d_v).transpose(1,2)  # v_s: [batch_size x n_heads x len_k x d_v]

        # attn_mask形状是[batch_size, len_q, len_k]，然后得到新的attn_mask:[batch_size, n_heads, len_q, len_k]，就是把pad信息重复了n个头上
        attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1)

        # context: [batch_size x n_heads x len_q x d_v], attn: [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]
        context, attn = ScaledDotProductAttention()(q_s, k_s, v_s, attn_mask)
        context = context.transpose(1, 2).contiguous().view(batch_size, -1, n_heads * d_v) # context: [batch_size x len_q x n_heads * d_v]
        output = self.linear(context)
        return self.layer_norm(output + residual), attn # output: [batch_size x len_q x d_model]

class ScaledDotProductAttention(nn.Module):
    def __init__(self):
        super(ScaledDotProductAttention, self).__init__()

    def forward(self, Q, K, V, attn_mask):
        # Q:[batch_size x n_heads x len_q x d_k]  K： [batch_size x n_heads x len_k x d_k]  V: [batch_size x n_heads x len_k x d_v]
        scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_k) # scores : [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]
        # 把被mask的地方置为无限小，softmax之后基本就是0
        scores.masked_fill_(attn_mask, -1e9)
        attn = nn.Softmax(dim=-1)(scores)
        context = torch.matmul(attn, V)
        
        return context, attn

class PoswiseFeedForwardNet(nn.Module):
    def __init__(self):
        super(PoswiseFeedForwardNet, self).__init__()
        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
        self.layer_norm = nn.LayerNorm(d_model)

    def forward(self, inputs):
        residual = inputs # inputs : [batch_size, len_q, d_model]
        output = nn.ReLU()(self.conv1(inputs.transpose(1, 2)))
        output = self.conv2(output).transpose(1, 2)
        return self.layer_norm(output + residual)

class Decoder(nn.Module):
    def __init__(self):
        super(Decoder, self).__init__()
        self.tgt_emb = nn.Embedding(tgt_vocab_size, d_model)
        self.pos_emb = nn.Embedding.from_pretrained(get_sinusoid_encoding_table(tgt_len+1, d_model),freeze=True)
        self.layers = nn.ModuleList([DecoderLayer() for _ in range(n_layers)])

    def forward(self, dec_inputs, enc_inputs, enc_outputs): # dec_inputs : [batch_size x target_len]
        dec_outputs = self.tgt_emb(dec_inputs) + self.pos_emb(torch.LongTensor([[5,1,2,3,4]]))
        dec_self_attn_pad_mask = get_attn_pad_mask(dec_inputs, dec_inputs)
        dec_self_attn_subsequent_mask = get_attn_subsequent_mask(dec_inputs)
        dec_self_attn_mask = torch.gt((dec_self_attn_pad_mask + dec_self_attn_subsequent_mask), 0)

        dec_enc_attn_mask = get_attn_pad_mask(dec_inputs, enc_inputs)

        dec_self_attns, dec_enc_attns = [], []
        for layer in self.layers:
            dec_outputs, dec_self_attn, dec_enc_attn = layer(dec_outputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask)
            dec_self_attns.append(dec_self_attn)
            dec_enc_attns.append(dec_enc_attn)
        return dec_outputs, dec_self_attns, dec_enc_attns

class DecoderLayer(nn.Module):
    def __init__(self):
        super(DecoderLayer, self).__init__()
        self.dec_self_attn = MultiHeadAttention()
        self.dec_enc_attn = MultiHeadAttention()
        self.pos_ffn = PoswiseFeedForwardNet()

    def forward(self, dec_inputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask):
        dec_outputs, dec_self_attn = self.dec_self_attn(dec_inputs, dec_inputs, dec_inputs, dec_self_attn_mask)
        dec_outputs, dec_enc_attn = self.dec_enc_attn(dec_outputs, enc_outputs, enc_outputs, dec_enc_attn_mask)
        dec_outputs = self.pos_ffn(dec_outputs)
        return dec_outputs, dec_self_attn, dec_enc_attn